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Merge pull request #131 from bryanguarente/main
Updated RGBs for MTG and new NOAA RGBs
2 parents 8d06efe + 895dd8d commit 3917c5e

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‎src/goes2go/accessors.py‎

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@@ -741,6 +741,75 @@ def DayCloudPhase(self):
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return ds["DayCloudPhase"]
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def DayCloudPhaseEUMETSAT(self):
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"""Create the Day Cloud Phase EUMETSAT RGB.
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(See `Quick Guide <https://eumetrain.org/sites/default/files/2023-01/CloudPhaseRGB.pdf>`__ for reference)
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.. image:: /_static/DayCloudPhaseEUMETSAT.png
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"""
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ds = self._obj
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# Load the three channels into appropriate R, G, and B variables
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R, G, B = self._load_RGB_channels((5, 6, 2))
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# _normalize each channel by the appropriate range of values. (Clipping happens inside function)
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R = _normalize(R, 0, .5)
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G = _normalize(G, 0, .5)
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B = _normalize(B, 0, 1)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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ds["DayCloudPhaseEUMETSAT"] = (("y", "x", "rgb"), RGB)
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ds["rgb"] = ["R", "G", "B"]
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ds["DayCloudPhaseEUMETSAT"].attrs["Quick Guide"] = (
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"https://eumetrain.org/sites/default/files/2023-01/CloudPhaseRGB.pdf"
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)
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ds["DayCloudPhaseEUMETSAT"].attrs["long_name"] = "Day Cloud Phase EUMETSAT"
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return ds["DayCloudPhaseEUMETSAT"]
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def DayCloudType(self):
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"""Create the Day Cloud Phase Type RGB.
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(See `Quick Guide <https://eumetrain.org/sites/default/files/2021-05/CloudTypeRGB.pdf>`__ for reference)
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.. image:: /_static/DayCloudType.png
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"""
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ds = self._obj
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# Load the three channels into appropriate R, G, and B variables
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R, G, B = self._load_RGB_channels((4, 2, 5))
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# _normalize each channel by the appropriate range of values. (Clipping happens inside function)
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R = _normalize(R, 0, .1)
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G = _normalize(G, 0, .8)
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B = _normalize(B, 0, .8)
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# Apply the gamma correction to Red channel.
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# corrected_value = value^(1/gamma)
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gamma = 1.5
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R = _gamma_correction(R, gamma)
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gamma = .75
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G = _gamma_correction(G, gamma)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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ds["DayCloudType"] = (("y", "x", "rgb"), RGB)
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ds["rgb"] = ["R", "G", "B"]
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ds["DayCloudType"].attrs["Quick Guide"] = (
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"https://eumetrain.org/sites/default/files/2021-05/CloudTypeRGB.pdf"
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)
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ds["DayCloudType"].attrs["long_name"] = "Day Cloud Type"
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return ds["DayCloudType"]
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def DayConvection(self):
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"""Create the Day Convection RGB.
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@@ -1309,3 +1378,40 @@ def SeaSpray(self, **kwargs):
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ds["SeaSpray"].attrs["long_name"] = "Sea Spray"
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return ds["SeaSpray"]
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def BlowingSnow(self, **kwargs):
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"""Create the Blowing Snow RGB.
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(See `Quick Guide <https://rammb2.cira.colostate.edu/wp-content/uploads/2024/11/GOES-BlowingSnowRGB1_QuickGuide_24April2024.pdf>`__ for reference)
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.. image:: /_static/BlowingSnow.png
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"""
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ds = self._obj
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# Load the three channels into appropriate R, G, and B variables
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R = ds["CMI_C02"].data
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G = ds["CMI_C05"].data
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B = ds["CMI_C07"].data - ds["CMI_C13"].data
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# Normalize each channel by the appropriate range of values. e.g. R = (R-minimum)/(maximum-minimum)
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R = _normalize(R, 0, .5) # values for this channel go from 0 to 1.
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G = _normalize(G, 0, 0.2) # values for this channel go from 0 to 1.
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B = _normalize(B, 0, 30)
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# Apply a gamma correction to each R, G, B channel
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R = _gamma_correction(R, 1/0.7)
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G = _gamma_correction(G, 1.0)
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B = _gamma_correction(B, 1/0.7)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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ds["BlowingSnow"] = (("y", "x", "rgb"), RGB)
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ds["rgb"] = ["R", "G", "B"]
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ds["BlowingSnow"].attrs["Quick Guide"] = (
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"https://rammb2.cira.colostate.edu/wp-content/uploads/2024/11/GOES-BlowingSnowRGB1_QuickGuide_24April2024.pdf"
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)
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ds["BlowingSnow"].attrs["long_name"] = "Blowing Snow"
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return ds["BlowingSnow"]

‎src/goes2go/rgb.py‎

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@@ -20,7 +20,10 @@
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- TrueColor
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- FireTemperature
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- AirMass
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- BlowingSnow
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- DayCloudPhase
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- DayCloudPhaseEUMETSAT
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- DayCloudType
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- DayConvection
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- DayCloudConvection
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- DayLandCloud
@@ -30,6 +33,7 @@
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- DaySnowFog
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- NighttimeMicrophysics
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- Dust
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- SeaSpray
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- SulfurDioxide
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- Ash
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- SplitWindowDifference
@@ -562,6 +566,42 @@ def AirMass(C, **kwargs):
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return rgb_as_dataset(C, RGB, "Air Mass", **kwargs)
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def BlowingSnow(C, **kwargs):
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"""
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Blowing Snow RGB:
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(See `Quick Guide <https://rammb2.cira.colostate.edu/wp-content/uploads/2024/11/GOES-BlowingSnowRGB1_QuickGuide_24April2024.pdf>`__ for reference)
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.. image:: /_static/BlowingSnow.png
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Parameters
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----------
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C : xarray.Dataset
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A GOES ABI multichannel file opened with xarray.
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\*\*kwargs :
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Keyword arguments for ``rgb_as_dataset`` function.
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- latlon : derive latitude and longitude of each pixel
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"""
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# Load the three channels into appropriate R, G, and B variables
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R = C["CMI_C02"].data
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G = C["CMI_C05"].data
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B = C["CMI_C07"].data - C["CMI_C13"].data
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# Normalize each channel by the appropriate range of values. e.g. R = (R-minimum)/(maximum-minimum)
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R = normalize(R, 0, 0.5)
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G = normalize(G, 0, 0.2)
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B = normalize(B, 0, 30)
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# Apply the gamma correction to Red channel.
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# corrected_value = value^(1/gamma)
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gamma = 1/.7
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R = gamma_correction(R, gamma)
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B = gamma_correction(B, gamma)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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return rgb_as_dataset(C, RGB, "Blowing Snow", **kwargs)
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def DayCloudPhase(C, **kwargs):
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"""
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return rgb_as_dataset(C, RGB, "Day Cloud Phase", **kwargs)
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def DayCloudPhaseEUMETSAT(C, **kwargs):
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"""
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Day Cloud Phase EUMETSAT RGB:
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(See `Quick Guide <https://eumetrain.org/sites/default/files/2023-01/CloudPhaseRGB.pdf>`__ for reference)
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.. image:: /_static/DayCloudPhaseEUMETSAT.png
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Parameters
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----------
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C : xarray.Dataset
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A GOES ABI multichannel file opened with xarray.
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\*\*kwargs :
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Keyword arguments for ``rgb_as_dataset`` function.
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- latlon : derive latitude and longitude of each pixel
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"""
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# Load the three channels into appropriate R, G, and B variables
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R, G, B = load_RGB_channels(C, (5, 6, 2))
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# Normalize each channel by the appropriate range of values. (Clipping happens inside function)
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R = normalize(R, 0, .5)
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G = normalize(G, 0, .5)
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B = normalize(B, 0, 1)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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return rgb_as_dataset(C, RGB, "Day Cloud Phase EUMETSAT", **kwargs)
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def DayCloudType(C, **kwargs):
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"""
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Day Cloud Type RGB:
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(See `Quick Guide <https://eumetrain.org/sites/default/files/2021-05/CloudTypeRGB.pdf>`__ for reference)
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.. image:: /_static/DayCloudType.png
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Parameters
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----------
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C : xarray.Dataset
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A GOES ABI multichannel file opened with xarray.
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\*\*kwargs :
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Keyword arguments for ``rgb_as_dataset`` function.
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- latlon : derive latitude and longitude of each pixel
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"""
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# Load the three channels into appropriate R, G, and B variables
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R, G, B = load_RGB_channels(C, (4, 2, 5))
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# Normalize each channel by the appropriate range of values. (Clipping happens inside function)
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R = normalize(R, 0, .1)
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G = normalize(G, 0, .8)
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B = normalize(B, 0, .8)
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# Apply the gamma correction to Red channel.
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# corrected_value = value^(1/gamma)
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gamma = 1.5
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R = gamma_correction(R, gamma)
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gamma = .75
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G = gamma_correction(G, gamma)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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return rgb_as_dataset(C, RGB, "Day Cloud Type", **kwargs)
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def DayConvection(C, **kwargs):
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"""
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return rgb_as_dataset(C, RGB, "Dust", **kwargs)
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def SeaSpray(C, **kwargs):
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"""
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Sea Spray RGB:
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(See `Quick Guide <https://rammb.cira.colostate.edu/training/visit/quick_guides/VIIRS_Sea_Spray_RGB_Quick_Guide_v2.pdf
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>`__ for reference)
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.. image:: /_static/SeaSpray.png
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Parameters
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----------
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C : xarray.Dataset
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A GOES ABI multichannel file opened with xarray.
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\*\*kwargs :
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Keyword arguments for ``rgb_as_dataset`` function.
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- latlon : derive latitude and longitude of each pixel
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"""
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# Load the three channels into appropriate R, G, and B variables
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R = C["CMI_C07"].data - C["CMI_C13"].data
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G = C["CMI_C03"].data
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B = C["CMI_C02"].data
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# Normalize values
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R = normalize(R, 0, 5)
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G = normalize(G, .01, .09)
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B = normalize(B, .02, .12)
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# Apply a gamma correction to the image
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gamma = 1/.6
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G = gamma_correction(G, gamma)
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B = gamma_correction(B, gamma)
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# The final RGB array :)
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RGB = np.dstack([R, G, B])
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return rgb_as_dataset(C, RGB, "Sea Spray", **kwargs)
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def SulfurDioxide(C, **kwargs):
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"""

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